Classifier fitness based on accuracy
Evolutionary Computation, 3 (2):
149--175 (1995)DOI: 10.1162/evco.1995.3.2.149
Abstract
In many classifier systems, the classifier strength parameter serves
as a predictor of future payoff and as the classifier's fitness for
the genetic algorithm. We investigate a classifier system, XCS, in
which each classifier maintains a prediction of expected payoff,
but the classifier's fitness is given by a measure of the prediction's
accuracy. The system executes the genetic algorithm in niches defined
by the match sets, instead of panmictically. These aspects of XCS
result in its population tending to form a complete and accurate
mapping $X A \right-arrow P$ from inputs and actions to payoff
predictions. Further, XCS tends to evolve classifiers that are maximally
general, subject to an accuracy criterion. Besides introducing a
new direction for classifier system research, these properties of
XCS make it suitable for a wide range of reinforcement learning situations
where generalization over states is desirable.
